Associating parking areas with destinations

ABSTRACT

A method and apparatus for associating parking areas with destinations may include a vehicle identifying transportation network information including a primary destination and parking area information representing a plurality of parking areas, such that the parking area information includes automatically generated parking area association information describing an association between at least one parking area and the primary destination. The vehicle may determine a target parking area from the plurality of parking areas for the primary destination based on the transportation network information, and identify a route from an origin to the target parking area in the vehicle transportation network using the transportation network information. The vehicle may include a trajectory controller configured to operate the vehicle to travel from the origin to the target parking area using the route.

TECHNICAL FIELD

This disclosure relates to vehicle routing and navigation.

BACKGROUND

A vehicle may include a control system that may generate and maintainthe route of travel and may control the vehicle to traverse the route oftravel. An autonomous vehicle may be controlled autonomously, withoutdirect human intervention, to traverse a route of travel from an originto a destination. Accordingly, a method and apparatus for associatingparking areas with destinations may be advantageous.

SUMMARY

Disclosed herein are aspects, features, elements, implementations, andembodiments of associating parking areas with destinations.

An aspect of the disclosed embodiments is a vehicle for associatingparking areas with destinations. The vehicle may include a processorconfigured to execute instructions stored on a non-transitory computerreadable medium to identify transportation network informationrepresenting a vehicle transportation network, the vehicletransportation network including a primary destination, whereinidentifying the transportation network information includes identifyingthe transportation network information such that the transportationnetwork information includes parking area information representing aplurality of parking areas, wherein each parking area from the pluralityof parking areas corresponds with a respective location in the vehicletransportation network, and such that the parking area informationincludes automatically generated parking area association informationdescribing an association between at least one parking area from theplurality of parking areas and the primary destination. The processormay be configured to execute instructions stored on a non-transitorycomputer readable medium to determine a target parking area from theplurality of parking areas for the primary destination based on thetransportation network information, and identify a route from an originto the target parking area in the vehicle transportation network usingthe transportation network information. The vehicle may include atrajectory controller configured to operate the vehicle to travel fromthe origin to the target parking area using the route.

Another aspect of the disclosed embodiments is a vehicle for associatingparking areas with destinations. The vehicle may include a processorconfigured to execute instructions stored on a non-transitory computerreadable medium to identify transportation network informationrepresenting a vehicle transportation network, the vehicletransportation network including a primary destination, whereinidentifying the transportation network information includes identifyingthe transportation network information such that the transportationnetwork information includes parking area information representing aplurality of parking areas, wherein each parking area from the pluralityof parking areas corresponds with a respective location in the vehicletransportation network, and such that the parking area informationincludes automatically generated parking area association informationdescribing an association between at least one parking area from theplurality of parking areas and the primary destination, such that theautomatically generated parking area association information is based onoperating information for a plurality of vehicles and such that theautomatically generated parking area association information isdetermined by filtering the operating information. The processor may beconfigured to execute instructions stored on a non-transitory computerreadable medium to filter the operating information by identifying avehicle from the plurality of vehicles, identifying a parking operationfor the vehicle, identifying a location corresponding to the parkingoperation as a candidate parking location, wherein the candidate parkinglocation corresponds with the at least one parking area from theplurality of parking areas, identifying a passenger associated with thevehicle, identifying a destination operation for the passenger,identifying a location corresponding to the destination operation as adestination location, wherein a location of the primary destinationcorresponds with the destination location, and including, in theautomatically generated parking area association information,automatically generated parking area association information thatdescribes an association between the primary destination and a parkingarea corresponding to the candidate parking location. The processor maybe configured to execute instructions stored on a non-transitorycomputer readable medium to determine a target parking area from theplurality of parking areas for the primary destination based on thetransportation network information, and identify a route from an originto the target parking area in the vehicle transportation network usingthe transportation network information. The vehicle may include atrajectory controller configured to operate the vehicle to travel fromthe origin to the target parking area using the route.

Another aspect of the disclosed embodiments is a system for generatingvehicle transportation network information. The system may include amemory including a non-transitory computer readable medium, and aprocessor configured to execute instructions stored on thenon-transitory computer readable medium to generating vehicletransportation network information. The processor may be configured toexecute instructions stored on a non-transitory computer readable mediumto identify vehicle transportation network information representing avehicle transportation network, and automatically generate parking areaassociation information by filtering operating information for aplurality of vehicles. The operating information may include vehicleoperating information, wherein the vehicle operating information isinformation reported by the plurality of vehicles, wherein the vehicleoperating information includes a plurality of vehicle operations thatincludes the parking operation, wherein each vehicle operation from theplurality of vehicle operations is associated with a respective vehiclefrom the plurality of vehicles, and wherein each parking area from theplurality of parking areas corresponds with a respective vehicleoperation from the plurality of vehicle operations, and wherein thevehicle operating information includes passenger information thatidentifies a plurality of passengers such that each passenger from theplurality of passengers is associated with a respective vehicle from theplurality of vehicles. The operating information may includesupplementary vehicle location information, wherein the supplementaryvehicle location information is information reported by a plurality ofinfrastructure devices in response to detecting a respective vehiclefrom the plurality of vehicles, and wherein each infrastructure devicefrom the plurality of infrastructure devices is associated with arespective location in the vehicle transportation network, and whereinthe supplementary vehicle location information includes a plurality ofsupplementary vehicle parking locations, wherein each supplementaryvehicle parking location from the plurality of supplementary vehicleparking locations is associated with a respective vehicle from theplurality of vehicles, and wherein each supplementary vehicle parkinglocation from the plurality of supplementary vehicle parking locationscorresponds with a respective parking operation from the plurality ofvehicle operations. The operating information may include non-vehicleoperating information, wherein the non-vehicle operating informationincludes a plurality of non-vehicle operations, wherein each non-vehicleoperation from the plurality of non-vehicle operations includes locationinformation reported by a portable device associated with a passengerfrom the plurality of passengers or location information reported to athird party computing system for a user, wherein the non-vehicleoperating information includes an association between the user and apassenger from the plurality of passengers. The processor may beconfigured to execute instructions stored on a non-transitory computerreadable medium to filter the operating information by identifying avehicle from the plurality of vehicles, identifying a parking operationfor the vehicle, identifying a location corresponding to the parkingoperation as a candidate parking location, identifying a passengerassociated with the vehicle, identifying a destination operation for thepassenger, identifying a location corresponding to the destinationoperation as a destination, and including, in the automaticallygenerated parking area association information, automatically generatedparking area association information that describes an associationbetween a location representing the destination and a parking areacorresponding to the candidate parking location. The processor may beconfigured to execute instructions stored on a non-transitory computerreadable medium to update the vehicle transportation network informationbased on the automatically generated parking area associationinformation.

Variations in these and other aspects, features, elements,implementations, and embodiments of the methods, apparatus, procedures,and algorithms disclosed herein are described in further detailhereafter.

BRIEF DESCRIPTION OF THE DRAWINGS

The various aspects of the methods and apparatuses disclosed herein willbecome more apparent by referring to the examples provided in thefollowing description and drawings in which:

FIG. 1 is a diagram of an example of a portion of an autonomous vehiclein which the aspects, features, and elements disclosed herein may beimplemented;

FIG. 2 is a diagram of an example of a portion of an autonomous vehicletransportation and communication system in which the aspects, features,and elements disclosed herein may be implemented;

FIG. 3 is a diagram of a portion of a vehicle transportation network inaccordance with this disclosure;

FIG. 4 is a diagram of another portion of a vehicle transportationnetwork in accordance with this disclosure;

FIG. 5 is a diagram of a method of autonomous vehicle navigation androuting in accordance with this disclosure;

FIG. 6 is a diagram of a method of automatically generating parking areaassociation information in accordance with this disclosure; and

FIG. 7 is a diagram of a method of associating non-vehicle operatinginformation with vehicle operating information in accordance with thisdisclosure.

DETAILED DESCRIPTION

An autonomous vehicle may travel from a point of origin to a destinationin a vehicle transportation network without human intervention. Theautonomous vehicle may include a controller, which may performautonomous vehicle routing and navigation. The controller may generate aroute of travel from the origin to the destination based on vehicleinformation, environment information, vehicle transportation networkinformation representing the vehicle transportation network, or acombination thereof. The controller may output the route of travel to atrajectory controller that may operate the vehicle to travel from theorigin to the destination using the generated route.

In some embodiments, the vehicle transportation network information mayomit information expressly identifying a parking area for thedestination, and parking area information may be automatically generatedbased on operating information, which may include vehicle operationinformation, such as information reported by one or more vehicles,supplementary vehicle location information, such as information for thevehicles reported by vehicle infrastructure units, and non-vehicleoperating information, such as information reported by a portable deviceassociated with a passenger of the vehicle, or social media informationassociated with a passenger of the vehicle.

In some embodiments, the operating information may be filtered, orotherwise processed, to correlate parking area information for a vehiclewith destination location information for a passenger of the vehicle.The correlated parking area information may be clustered arounddestinations, and one or more parking areas may associated with adestination in the vehicle transportation network information.

As used herein, the terminology “computer” or “computing device”includes any unit, or combination of units, capable of performing anymethod, or any portion or portions thereof, disclosed herein.

As used herein, the terminology “processor” indicates one or moreprocessors, such as one or more special purpose processors, one or moredigital signal processors, one or more microprocessors, one or morecontrollers, one or more microcontrollers, one or more ApplicationSpecific Integrated Circuits, one or more Application Specific StandardProducts; one or more Field Programmable Gate Arrays, any other type orcombination of integrated circuits, one or more state machines, or anycombination thereof.

As used herein, the terminology “memory” indicates any computer-usableor computer-readable medium or device that can tangibly contain, store,communicate, or transport any signal or information that may be used byor in connection with any processor. For example, a memory may be one ormore read only memories (ROM), one or more random access memories (RAM),one or more registers, one or more cache memories, one or moresemiconductor memory devices, one or more magnetic media, one or moreoptical media, one or more magneto-optical media, or any combinationthereof.

As used herein, the terminology “instructions” may include directions orexpressions for performing any method, or any portion or portionsthereof, disclosed herein, and may be realized in hardware, software, orany combination thereof. For example, instructions may be implemented asinformation, such as a computer program, stored in memory that may beexecuted by a processor to perform any of the respective methods,algorithms, aspects, or combinations thereof, as described herein. Insome embodiments, instructions, or a portion thereof, may be implementedas a special purpose processor, or circuitry, that may includespecialized hardware for carrying out any of the methods, algorithms,aspects, or combinations thereof, as described herein. In someimplementations, portions of the instructions may be distributed acrossmultiple processors on a single device, on multiple devices, which maycommunicate directly or across a network such as a local area network, awide area network, the Internet, or a combination thereof.

As used herein, the terminology “example”, “embodiment”,“implementation”, “aspect”, “feature”, or “element” indicate serving asan example, instance, or illustration. Unless expressly indicated, anyexample, embodiment, implementation, aspect, feature, or element isindependent of each other example, embodiment, implementation, aspect,feature, or element and may be used in combination with any otherexample, embodiment, implementation, aspect, feature, or element.

As used herein, the terminology “determine” and “identify”, or anyvariations thereof, includes selecting, ascertaining, computing, lookingup, receiving, determining, establishing, obtaining, or otherwiseidentifying or determining in any manner whatsoever using one or more ofthe devices shown and described herein.

As used herein, the terminology “or” is intended to mean an inclusive“or” rather than an exclusive “or”. That is, unless specified otherwise,or clear from context, “X includes A or B” is intended to indicate anyof the natural inclusive permutations. That is, if X includes A; Xincludes B; or X includes both A and B, then “X includes A or B” issatisfied under any of the foregoing instances. In addition, thearticles “a” and “an” as used in this application and the appendedclaims should generally be construed to mean “one or more” unlessspecified otherwise or clear from context to be directed to a singularform.

Further, for simplicity of explanation, although the figures anddescriptions herein may include sequences or series of steps or stages,elements of the methods disclosed herein may occur in various orders orconcurrently. Additionally, elements of the methods disclosed herein mayoccur with other elements not explicitly presented and described herein.Furthermore, not all elements of the methods described herein may berequired to implement a method in accordance with this disclosure.Although aspects, features, and elements are described herein inparticular combinations, each aspect, feature, or element may be usedindependently or in various combinations with or without other aspects,features, and elements.

FIG. 1 is a diagram of an example of an autonomous vehicle in which theaspects, features, and elements disclosed herein may be implemented. Insome embodiments, an autonomous vehicle 1000 may include a chassis 1100,a powertrain 1200, a controller 1300, wheels 1400, or any other elementor combination of elements of an autonomous vehicle. Although theautonomous vehicle 1000 is shown as including four wheels 1400 forsimplicity, any other propulsion device or devices, such as a propelleror tread, may be used. In FIG. 1, the lines interconnecting elements,such as the powertrain 1200, the controller 1300, and the wheels 1400,indicate that information, such as data or control signals, power, suchas electrical power or torque, or both information and power, may becommunicated between the respective elements. For example, thecontroller 1300 may receive power from the powertrain 1200 and maycommunicate with the powertrain 1200, the wheels 1400, or both, tocontrol the autonomous vehicle 1000, which may include accelerating,decelerating, steering, or otherwise controlling the autonomous vehicle1000.

The powertrain 1200 may include a power source 1210, a transmission1220, a steering unit 1230, an actuator 1240, or any other element orcombination of elements of a powertrain, such as a suspension, a driveshaft, axles, or an exhaust system. Although shown separately, thewheels 1400 may be included in the powertrain 1200.

The power source 1210 may include an engine, a battery, or a combinationthereof. The power source 1210 may be any device or combination ofdevices operative to provide energy, such as electrical energy, thermalenergy, or kinetic energy. For example, the power source 1210 mayinclude an engine, such as an internal combustion engine, an electricmotor, or a combination of an internal combustion engine and an electricmotor, and may be operative to provide kinetic energy as a motive forceto one or more of the wheels 1400. In some embodiments, the power source1400 may include a potential energy unit, such as one or more dry cellbatteries, such as nickel-cadmium (NiCd), nickel-zinc (NiZn), nickelmetal hydride (NiMH), lithium-ion (Li-ion); solar cells; fuel cells; orany other device capable of providing energy.

The transmission 1220 may receive energy, such as kinetic energy, fromthe power source 1210, and may transmit the energy to the wheels 1400 toprovide a motive force. The transmission 1220 may be controlled by thecontrol unit 1300 the actuator 1240 or both. The steering unit 1230 maybe controlled by the control unit 1300 the actuator 1240 or both and maycontrol the wheels 1400 to steer the autonomous vehicle. The vehicleactuator 1240 may receive signals from the controller 1300 and mayactuate or control the power source 1210, the transmission 1220, thesteering unit 1230, or any combination thereof to operate the autonomousvehicle 1000.

In some embodiments, the controller 1300 may include a location unit1310, an electronic communication unit 1320, a processor 1330, a memory1340, a user interface 1350, a sensor 1360, an electronic communicationinterface 1370, or any combination thereof. Although shown as a singleunit, any one or more elements of the controller 1300 may be integratedinto any number of separate physical units. For example, the userinterface 1350 and processor 1330 may be integrated in a first physicalunit and the memory 1340 may be integrated in a second physical unit.Although not shown in FIG. 1, the controller 1300 may include a powersource, such as a battery. Although shown as separate elements, thelocation unit 1310, the electronic communication unit 1320, theprocessor 1330, the memory 1340, the user interface 1350, the sensor1360, the electronic communication interface 1370, or any combinationthereof may be integrated in one or more electronic units, circuits, orchips.

In some embodiments, the processor 1330 may include any device orcombination of devices capable of manipulating or processing a signal orother information now-existing or hereafter developed, including opticalprocessors, quantum processors, molecular processors, or a combinationthereof. For example, the processor 1330 may include one or more specialpurpose processors, one or more digital signal processors, one or moremicroprocessors, one or more controllers, one or more microcontrollers,one or more integrated circuits, one or more an Application SpecificIntegrated Circuits, one or more Field Programmable Gate Array, one ormore programmable logic arrays, one or more programmable logiccontrollers, one or more state machines, or any combination thereof. Theprocessor 1330 may be operatively coupled with the location unit 1310,the memory 1340, the electronic communication interface 1370, theelectronic communication unit 1320, the user interface 1350, the sensor1360, the powertrain 1200, or any combination thereof. For example, theprocessor may be operatively couple with the memory 1340 via acommunication bus 1380.

The memory 1340 may include any tangible non-transitory computer-usableor computer-readable medium, capable of, for example, containing,storing, communicating, or transporting machine readable instructions,or any information associated therewith, for use by or in connectionwith the processor 1330. The memory 1340 may be, for example, one ormore solid state drives, one or more memory cards, one or more removablemedia, one or more read only memories, one or more random accessmemories, one or more disks, including a hard disk, a floppy disk, anoptical disk, a magnetic or optical card, or any type of non-transitorymedia suitable for storing electronic information, or any combinationthereof.

The communication interface 1370 may be a wireless antenna, as shown, awired communication port, an optical communication port, or any otherwired or wireless unit capable of interfacing with a wired or wirelesselectronic communication medium 1500. Although FIG. 1 shows thecommunication interface 1370 communicating via a single communicationlink, a communication interface may be configured to communicate viamultiple communication links. Although FIG. 1 shows a singlecommunication interface 1370, an autonomous vehicle may include anynumber of communication interfaces.

The communication unit 1320 may be configured to transmit or receivesignals via a wired or wireless medium 1500, such as via thecommunication interface 1370. Although not explicitly shown in FIG. 1,the communication unit 1320 may be configured to transmit, receive, orboth via any wired or wireless communication medium, such as radiofrequency (RF), ultra violet (UV), visible light, fiber optic, wireline, or a combination thereof. Although FIG. 1 shows a singlecommunication unit 1320 and a single communication interface 1370, anynumber of communication units and any number of communication interfacesmay be used.

The location unit 1310 may determine geolocation information, such aslongitude, latitude, elevation, direction of travel, or speed, of theautonomous vehicle 1000. For example, the location unit may include aglobal positioning system (GPS) unit, a radio triangulation unit, or acombination thereof. The location unit 1310 can be used to obtaininformation that represents, for example, a current heading of theautonomous vehicle 1000, a current position of the autonomous vehicle1000 in two or three dimensions, a current angular orientation of theautonomous vehicle 1000, or a combination thereof.

The user interface 1350 may include any unit capable of interfacing witha person, such as a virtual or physical keypad, a touchpad, a display, atouch display, a speaker, a microphone, a video camera, a sensor, aprinter, or any combination thereof. The user interface 1350 may beoperatively coupled with the processor 1330, as shown, or with any otherelement of the controller 1300. Although shown as a single unit, theuser interface 1350 may include one or more physical units. For example,the user interface 1350 may include an audio interface for performingaudio communication with a person, and a touch display for performingvisual and touch based communication with the person.

The sensor 1360 may include one or more sensors, such as an array ofsensors, which may be operable to provide information that may be usedto control the autonomous vehicle. The sensors 1360 may provideinformation regarding current operating characteristics of the vehicle.The sensors 1360 can include, for example, a speed sensor, accelerationsensors, a steering angle sensor, traction-related sensors,braking-related sensors, or any sensor, or combination of sensors, thatis operable to report information regarding some aspect of the currentdynamic situation of the autonomous vehicle 1000.

In some embodiments, the sensors 1360 may include sensors that areoperable to obtain information regarding the physical environmentsurrounding the autonomous vehicle 1000. For example, one or moresensors may detect road geometry and obstacles, such as fixed obstacles,vehicles, and pedestrians. In some embodiments, the sensors 1360 can beor include one or more video cameras, laser-sensing systems,infrared-sensing systems, acoustic-sensing systems, or any othersuitable type of on-vehicle environmental sensing device, or combinationof devices, now known or later developed. In some embodiments, thesensors 1360 and the location unit 1310 may be combined.

Although not shown separately, in some embodiments, the autonomousvehicle 1000 may include a trajectory controller. For example, thecontroller 1300 may include the trajectory controller. The trajectorycontroller may be operable to obtain information describing a currentstate of the autonomous vehicle 1000 and a rout planned for theautonomous vehicle 1000, and, based on this information, to determineand optimize a trajectory for the autonomous vehicle 1000. In someembodiments, the trajectory controller may output signals operable tocontrol the autonomous vehicle 1000 such that the autonomous vehicle1000 follows the trajectory that is determined by the trajectorycontroller. For example, the output of the trajectory controller can bean optimized trajectory that may be supplied to the powertrain 1200, thewheels 1400, or both. In some embodiments, the optimized trajectory canbe control inputs such as a set of steering angles, with each steeringangle corresponding to a point in time or a position. In someembodiments, the optimized trajectory can be one or more paths, lines,curves, or a combination thereof.

One or more of the wheels 1400 may be a steered wheel, which may bepivoted to a steering angle under control of the steering unit 1230, apropelled wheel, which may be torqued to propel the autonomous vehicle1000 under control of the transmission 1220, or a steered and propelledwheel that may steer and propel the autonomous vehicle 1000.

Although not shown in FIG. 1, an autonomous vehicle may include units,or elements not shown in FIG. 1, such as an enclosure, a Bluetooth®module, a frequency modulated (FM) radio unit, a Near FieldCommunication (NFC) module, a liquid crystal display (LCD) display unit,an organic light-emitting diode (OLED) display unit, a speaker, or anycombination thereof.

FIG. 2 is a diagram of an example of a portion of an autonomous vehicletransportation and communication system in which the aspects, features,and elements disclosed herein may be implemented. The autonomous vehicletransportation and communication system 2000 may include one or moreautonomous vehicles 2100, such as the autonomous vehicle 1000 shown inFIG. 1, which may travel via one or more portions of one or more vehicletransportation networks 2200, and may communicate via one or moreelectronic communication networks 2300. Although not explicitly shown inFIG. 2, an autonomous vehicle may traverse an area that is not expresslyor completely included in a vehicle transportation network, such as anoff-road area.

In some embodiments, the electronic communication network 2300 may be,for example, a multiple access system and may provide for communication,such as voice communication, data communication, video communication,messaging communication, or a combination thereof, between theautonomous vehicle 2100 and one or more communicating devices 2400. Forexample, an autonomous vehicle 2100 may receive information, such asinformation representing the vehicle transportation network 2200, from acommunicating device 2400 via the network 2300.

In some embodiments, an autonomous vehicle 2100 may communicate via awired communication link (not shown), a wireless communication link2310/2320, or a combination of any number of wired or wirelesscommunication links. For example, as shown, an autonomous vehicle 2100may communicate via a terrestrial wireless communication link 2310, viaa non-terrestrial wireless communication link 2320, or via a combinationthereof. In some implementations, a terrestrial wireless communicationlink 2310 may include an Ethernet link, a serial link, a Bluetooth link,an infrared (IR) link, an ultraviolet (UV) link, or any link capable ofproviding for electronic communication.

In some embodiments, the autonomous vehicle 2100 may communicate withthe communications network 2300 via an access point 2330. An accesspoint 2330, which may include a computing device, may be configured tocommunicate with an autonomous vehicle 2100, with a communicationnetwork 2300, with one or more communicating devices 2400, or with acombination thereof via wired or wireless communication links 2310/2340.For example, an access point 2330 may be a base station, a basetransceiver station (BTS), a Node-B, an enhanced Node-B (eNode-B), aHome Node-B (HNode-B), a wireless router, a wired router, a hub, arelay, a switch, or any similar wired or wireless device. Although shownas a single unit, an access point may include any number ofinterconnected elements.

In some embodiments, the autonomous vehicle 2100 may communicate withthe communications network 2300 via a satellite 2350, or othernon-terrestrial communication device. A satellite 2350, which mayinclude a computing device, may be configured to communicate with anautonomous vehicle 2100, with a communication network 2300, with one ormore communicating devices 2400, or with a combination thereof via oneor more communication links 2320/2360. Although shown as a single unit,a satellite may include any number of interconnected elements.

An electronic communication network 2300 may be any type of networkconfigured to provide for voice, data, or any other type of electroniccommunication. For example, the electronic communication network 2300may include a local area network (LAN), a wide area network (WAN), avirtual private network (VPN), a mobile or cellular telephone network,the Internet, or any other electronic communication system. Theelectronic communication network 2300 may use a communication protocol,such as the transmission control protocol (TCP), the user datagramprotocol (UDP), the internet protocol (IP), the real-time transportprotocol (RTP) the Hyper Text Transport Protocol (HTTP), or acombination thereof. Although shown as a single unit, an electroniccommunication network may include any number of interconnected elements.

In some embodiments, an autonomous vehicle 2100 may identify a portionor condition of the vehicle transportation network 2200. For example,the autonomous vehicle may include one or more on-vehicle sensors 2110,such as sensor 1360 shown in FIG. 1, which may include a speed sensor, awheel speed sensor, a camera, a gyroscope, an optical sensor, a lasersensor, a radar sensor, a sonic sensor, or any other sensor or device orcombination thereof capable of determining or identifying a portion orcondition of the vehicle transportation network 2200.

In some embodiments, an autonomous vehicle 2100 may traverse a portionor portions of one or more vehicle transportation networks 2200 usinginformation communicated via the network 2300, such as informationrepresenting the vehicle transportation network 2200, informationidentified by one or more on-vehicle sensors 2110, or a combinationthereof.

Although, for simplicity, FIG. 2 shows one autonomous vehicle 2100, onevehicle transportation network 2200, one electronic communicationnetwork 2300, and one communicating device 2400, any number ofautonomous vehicles, networks, or computing devices may be used. In someembodiments, the autonomous vehicle transportation and communicationsystem 2000 may include devices, units, or elements not shown in FIG. 2.Although the autonomous vehicle 2100 is shown as a single unit, anautonomous vehicle may include any number of interconnected elements.

FIG. 3 is a diagram of a portion of a vehicle transportation network inaccordance with this disclosure. A vehicle transportation network 3000may include one or more unnavigable areas 3100, such as a building, oneor more partially navigable areas, such as parking area 3200, one ormore navigable areas, such as roads 3300/3400, or a combination thereof.In some embodiments, an autonomous vehicle, such as the autonomousvehicle 1000 shown in FIG. 1 or the autonomous vehicle 2100 shown inFIG. 2, may traverse a portion or portions of the vehicle transportationnetwork 3000.

The vehicle transportation network may include one or more interchanges3210 between one or more navigable, or partially navigable, areas3200/3300/3400. For example, the portion of the vehicle transportationnetwork shown in FIG. 3 includes an interchange 3210 between the parkingarea 3200 and road 3400. In some embodiments, the parking area 3200 mayinclude parking slots 3220.

A portion of the vehicle transportation network, such as a road3300/3400 may include one or more lanes 3320/3340/3360/3420/3440, andmay be associated with one or more directions of travel, which areindicated by arrows in FIG. 3.

In some embodiments, a vehicle transportation network, or a portionthereof, such as the portion of the vehicle transportation network shownin FIG. 3, may be represented as vehicle transportation networkinformation. For example, vehicle transportation network information maybe expressed as a hierarchy of elements, such as markup languageelements, which may be stored in a database or file. For simplicity, theFigures herein depict vehicle transportation network informationrepresenting portions of a vehicle transportation network as diagrams ormaps; however, vehicle transportation network information may beexpressed in any computer-usable form capable of representing a vehicletransportation network, or a portion thereof. In some embodiments, thevehicle transportation network information may include vehicletransportation network control information, such as direction of travelinformation, speed limit information, toll information, gradeinformation, such as inclination or angle information, surface materialinformation, aesthetic information, or a combination thereof.

In some embodiments, a portion, or a combination of portions, of thevehicle transportation network may be identified as a point of interestor a destination. For example, the vehicle transportation networkinformation may identify the building 3100 as a point of interest, anautonomous vehicle may identify the point of interest as a destination,and the autonomous vehicle may travel from an origin to the destinationby traversing the vehicle transportation network.

In some embodiments, identifying a destination may include identifying alocation for the destination, which may be a discrete uniquelyidentifiable geolocation, such as the geographic location 3500 for thebuilding 3100. For example, the vehicle transportation network mayinclude a defined location, such as a street address, a postal address,a vehicle transportation network address, a longitude and latitude, or aGPS address, for the destination.

In some embodiments, a destination may be associated with one or moreentrances, such as the entrance 3600 shown in FIG. 3. In someembodiments, the vehicle transportation network information may includedefined or predicted entrance location information, such as informationidentifying a geolocation of an entrance associated with a destination.

In some embodiments, the vehicle transportation network may beassociated with, or may include, a pedestrian transportation network.For example, FIG. 3 includes a portion 3700 of a pedestriantransportation network, which may be a pedestrian walkway. In someembodiments, a pedestrian transportation network, or a portion thereof,such as the portion 3700 of the pedestrian transportation network shownin FIG. 3, may be represented as pedestrian transportation networkinformation. In some embodiments, the vehicle transportation networkinformation may include pedestrian transportation network information. Apedestrian transportation network may include pedestrian navigableareas. A pedestrian navigable area, such as a pedestrian walkway or asidewalk, may correspond with a non-navigable area of a vehicletransportation network. Although not shown separately in FIG. 3, apedestrian navigable area, such as a pedestrian crosswalk, maycorrespond with a navigable area, or a partially navigable area, of avehicle transportation network.

In some embodiments, a destination, such as the building 3100 may beassociated with a parking area, such as the parking area 3200. Forexample, the vehicle transportation network information may includedefined parking area information indicating that one or more parkingareas are associated with a destination. In some embodiments, thevehicle transportation network information may omit informationidentifying the parking area 3200 or information associating the parkingarea 3200 with a destination.

In an example, the vehicle transportation network information mayinclude information identifying the parking area 3200 as a navigable orpartially navigable portion of the vehicle transportation network, mayinclude information identifying the parking area 3200 as a parking area,and may include defined parking area association information describingan association between a destination, such as the building 3100, and theparking area 3200. Defined parking area association information may beparking area association information generated in response to user inputexpressly defining or creating the parking area association information.

In another example, the vehicle transportation network information mayinclude information identifying the parking area 3200 as a navigable orpartially navigable portion of the vehicle transportation network, mayinclude information identifying the parking area 3200 as a parking area,and may include automatically generated parking area associationinformation describing an association between a destination, such as thebuilding 3100, and the parking area 3200. Automatically generatedparking area association information may be parking area associationinformation generated automatically as described herein. In someembodiments, the vehicle transportation network information may omitinformation identifying an area as a parking area, and automaticallygenerating parking area association information may includeautomatically generating information identifying an area as a parkingarea.

FIG. 4 is a diagram of another portion of a vehicle transportationnetwork in accordance with this disclosure. The portion of the vehicletransportation network, as shown in FIG. 4, may include one or moreunnavigable areas 4100, such as a building, one or more navigable areas,such as roads 4200, one or more partially navigable areas, such asparking areas 4300/4310/4320/4330, or a combination thereof. In someembodiments, the vehicle transportation network information may includelocation information for a destination, such as the location 4110 forthe building 4100.

In some embodiments, a parking area may be associated with, or dedicatedto, a destination. For example, the parking area 3200 shown in FIG. 3may be dedicated to the building 3100 shown in FIG. 3. In someembodiments, a destination may not be associated with a parking areadedicated to the destination. For example, the parking areas4300/4310/4320/4330 shown in FIG. 4 may not be dedicated to a particularbuilding, destination, or point of interest.

In some embodiments, an association, or relationship, between a buildingand a parking area may be described in the vehicle transportationnetwork information as defined parking area association information. Insome embodiments, the vehicle transportation network information mayomit defined parking area association information, and an association,or relationship, between a building and a parking area may be describedin the vehicle transportation network information as automaticallygenerated parking area association information. For example, the vehicletransportation network information representing the portion of thevehicle transportation network shown in FIG. 4 may include automaticallygenerated parking area association information indicating that theparking areas 4300/4310/4320/4330 are parking areas for the building4100.

FIG. 5 is a diagram of a method of autonomous vehicle navigation androuting in accordance with this disclosure. Autonomous vehiclenavigation and routing may be implemented in an autonomous vehicle, suchas the autonomous vehicle 1000 shown in FIG. 1 or the autonomous vehicle2100 shown in FIG. 2. For example, the processor 1330 of the controller1300 of the autonomous vehicle 1000 shown in FIG. 1 may executeinstructions stored on the memory 1340 of the controller 1300 of theautonomous vehicle 1000 shown in FIG. 1 to perform autonomous vehiclenavigation and routing. Implementations of autonomous vehicle navigationand routing may include identifying vehicle transportation networkinformation at 5100, determining a target parking area at 5200,identifying a route at 5300, traveling at 5400, or a combinationthereof.

In some embodiments, vehicle transportation network information, such asthe vehicle transportation network information shown in FIG. 3 or thevehicle transportation network shown in FIG. 4, may be identified at5100. For example, an autonomous vehicle control unit, such as thecontroller 1300 shown in FIG. 1, may read the vehicle transportationnetwork information from a data storage unit, such as the memory 1340shown in FIG. 1, or may receive the vehicle transportation networkinformation from an external data source, such as the communicatingdevice 2400 shown in FIG. 2, via a communication system, such as theelectronic communication network 2300 shown in FIG. 2. In someembodiments, identifying the vehicle transportation network informationmay include transcoding or reformatting the vehicle transportationnetwork information, storing the reformatted vehicle transportationnetwork information, or both.

In some embodiments, the vehicle transportation network information mayinclude parking area information representing one or more parking areaswithin the vehicle transportation network. In some embodiments, theautonomous vehicle may identify the vehicle transportation networkinformation such that the vehicle transportation network informationincludes defined parking area association information, automaticallygenerated parking area association information, or a combinationthereof.

In some embodiments, a destination may be identified at 5200.Identifying a destination may include identifying a point of interest,such as the building 3100 shown in FIG. 3, or the location 4110 of thebuilding 4100 shown in FIG. 4, as a primary destination, and identifyinga target parking area for the point of interest as a secondarydestination, or identifying both a primary and a secondary destination.

In some embodiments, the target parking area for to the primarydestination within the vehicle transportation network may be identifiedat 5200 based on the vehicle transportation network information. Forexample, a building, such as the building 3100 shown in FIG. 3, may beidentified as the primary destination, and a parking area, such as theparking area 3200 shown in FIG. 3, may be identified as the targetparking area based on defined parking area association information. Insome embodiments, the target parking area may be identified based onautomatically generated parking area association information. Forexample, a building, such as the building 4100 shown in FIG. 4, may beidentified as the primary destination, and a target parking area, suchas one of the parking areas 4300/4310/4320/4330 shown in FIG. 4, may beidentified as the target parking area based on automatically generatedparking area association information.

A route may be generated at 5300. In some embodiments, generating theroute may include identifying an origin. For example, the origin mayindicate a target starting point, such as a current location of theautonomous vehicle. In some embodiments, identifying the origin mayinclude controlling a location unit, such as the location unit 1310shown in FIG. 1, to determine a current geographic location of theautonomous vehicle. In some embodiments, identifying the origin at 5300may include identifying vehicle transportation network informationcorresponding to the origin. For example, identifying the origin mayinclude identifying a road, road segment, lane, waypoint, or acombination thereof. In some embodiments, the current location of theautonomous vehicle may be a navigable non-road area or an area that isnot expressly or completely included in a vehicle transportationnetwork, such as an off-road area, and identifying the origin mayinclude identifying a road, road segment, lane, waypoint, or acombination thereof, near, or proximal to, the current location of theautonomous vehicle. Generating the route may include determining a routefrom the origin to the target parking area identified at 5200, orgenerating a route from the origin to a docking location associated withthe primary destination, and generating a route from the dockinglocation to the target parking area identified at 5200. For simplicityand clarity, the description herein refers to routing and navigationbetween an origin and a target parking area; however, routing andnavigation may include routing and navigation between the origin and adocking location associated with the primary destination and routing andnavigation between the docking location and one or more parking areas.

In some embodiments, generating the route may include generatingcandidate routes from the origin to the target parking area. In someembodiments, a candidate route may represent a unique or distinct routefrom the origin to the target parking area. For example, a candidateroute may include a unique or distinct combination of roads, roadsegments, lanes, waypoints, and interchanges.

In some embodiments, generating the route may include identifyingrouting states. In some embodiments, identifying routing states mayinclude identifying a routing state corresponding to each waypoint in acandidate route, for each of the candidate routes. For example, a firstrouting state may indicate a road, a road segment, a lane, a waypoint,or a combination thereof, in a first candidate route, and a secondrouting state may indicate the road, the road segment, the lane, thewaypoint, or the combination thereof, in a second candidate route.

In some embodiments, generating the route may include evaluating theexpected action costs for performing an action, such as transitioningfrom one routing state to another, which may correspond withtransitioning from one waypoint to another, and may represent theexpected cost of the autonomous vehicle traveling from one location,represented by the first waypoint, to another location, represented bythe second waypoint, during execution of the route. In some embodiments,an action may indicate a transition from a routing state to animmediately adjacent routing state, which may correspond withtransitioning from a waypoint to an immediately adjacent waypointwithout intersecting another waypoint, and may represent an autonomousvehicle traveling from a location, represented by the first waypoint, toanother location, represented by the immediately adjacent waypoint.

In some embodiments, an action cost may be determined based on thevehicle transportation network information. For example, within acandidate route, a first routing state may correspond with a firstwaypoint, which may correspond with a first location in the vehicletransportation network, a second routing state may correspond with asecond waypoint, which may correspond with second location in thevehicle transportation network, and the action cost may represent anestimated, predicted, or expected cost for the autonomous vehicle totravel from the first location to the second location. In someembodiments, action costs may be context dependent. For example, theaction cost for transitioning between two waypoints at one time of daymay be significant higher than the action costs for transitioningbetween the waypoints at another time of day.

In some embodiments, generating the route may include generatingprobability distributions. In some embodiments, generating theprobability distributions may include generating a probable costdistribution for performing an action, such as transitioning from onerouting state to another. Generating a probably cost distribution mayinclude determining a probability of successfully performing an action,the probability of failing to perform the action, determining multiplepossible costs for performing the action, determining probable costsassociating probabilities with possible costs, or a combination thereof.

In some embodiments, generating a probability distribution may includeusing a normal, or Gaussian, distribution, N(μ, σ), where μ indicatesthe mean of the normal distribution, and σ indicates the standarddeviation. The mean of the normal distribution and the standarddeviation may vary from one action to another. In some embodiments, thestandard deviation may be augmented based on an action cost uncertaintyvariance modifier, which may represent variation in the uncertainty ofaction costs.

In some embodiments, generating a probability distribution may includegenerating discrete cost probability combinations for an action. Forexample, for an action in a route, generating a probability distributionmay include generating a first probable cost as a combination of a firstaction cost, such as 45, and a first probability, such as 0.05, andgenerating a second probable cost as a combination of a second actioncost, such as 50, and a second probability, such as 0.08.

In some embodiments, generating a probability distribution may includeusing a liner model of resources and costs. For example, the probabilitydistribution for the travel time associated with an action may berepresented by piece-wise constant functions, and the costs forperforming an action may be represented by piece-wise linear functions.

In some embodiments, determining the action cost may include evaluatingcost metrics, such as a distance cost metric, a duration cost metric, afuel cost metric, an acceptability cost metric, or a combinationthereof. In some embodiments, the cost metrics may be determineddynamically or may be generated, stored, and accessed from memory, suchas in a database. In some embodiments, determining the action cost mayinclude calculating a cost function based on one or more of the metrics.For example, the cost function may be minimizing with respect to thedistance cost metric, minimizing with respect to the duration costmetric, minimizing with respect to the fuel cost metric, and maximizingwith respect to the acceptability cost metric.

A distance cost metric may represent a distance from a first locationrepresented by a first waypoint corresponding to a first routing stateto a second location represented by a second waypoint corresponding to asecond routing state.

A duration cost metric may represent a predicted duration for travelingfrom a first location represented by a first waypoint corresponding to afirst routing state to a second location represented by a secondwaypoint corresponding to a second routing state, and may be based oncondition information for the autonomous vehicle and the vehicletransportation network, which may include fuel efficiency information,expected initial speed information, expected average speed information,expected final speed information, road surface information, or any otherinformation relevant to travel duration.

A fuel cost metric may represent a predicted fuel utilization totransition from a first routing state to a second routing state, and maybe based on condition information for the autonomous vehicle and thevehicle transportation network, which may include fuel efficiencyinformation, expected initial speed information, expected average speedinformation, expected final speed information, road surface information,or any other information relevant to fuel cost.

An acceptability cost metric may represent a predicted acceptability fortraveling from a first location represented by a first waypointcorresponding to a first routing state to a second location representedby a second waypoint corresponding to a second routing state, and may bebased on condition information for the autonomous vehicle and thevehicle transportation network, which may include expected initial speedinformation, expected average speed information, expected final speedinformation, road surface information, aesthetic information, tollinformation, or any other information relevant to travel acceptability.In some embodiments, the acceptability cost metric may be based onacceptability factors. In some embodiments, an acceptability factor mayindicate that a location, which may include a specified road or area,such as an industrial area, or a road type, such as a dirt road or atoll road, has a low or negative acceptability, or an acceptabilityfactor may indicate that a location, such as road having a scenic view,has a high or positive acceptability factor.

In some embodiments, evaluating the cost metrics may include weightingthe cost metrics and calculating the action cost based on the weightedcost metrics. Weighting a cost metric may include identifying aweighting factor associated with the cost metric. For example,identifying a weighting factor may include accessing a record indicatingthe weighting factor and an association between the weighting factor andthe cost metric. In some embodiments, weighting a cost metric mayinclude generating a weighted cost metric based on the weighting factorand the cost metric. For example, a weighted cost metric may be aproduct of the weighting factor and the cost metric. In someembodiments, estimating the action cost may include calculating a sum ofcost metrics, or a sum of weighted cost metrics.

In some embodiments, generating the route may include identifying anoptimal route. Identifying the optimal route may include selecting acandidate route from the candidate routes based on the probabilitydistributions. For example, a candidate route having a minimal probableroute cost may be identified as the optimal route. In some embodiments,identifying the optimal route may include using a constant timestochastic control process, such as a hybrid Markov decision process.

In some embodiments, identifying the optimal route may include selectingthe minimum probable action cost from among an action cost probabilitydistribution for transitioning from a first routing state to a secondrouting state and an action cost probability distribution fortransitioning from the first routing state to a third routing state.

In some embodiments, identifying the optimal route may includegenerating a route cost probability distribution for a candidate routebased on the action cost probability distributions for each action inthe route. In some embodiments, identifying the optimal route mayinclude generating a route cost probability distribution for eachcandidate route and selecting the candidate route with the lowest, orminimum, probable route cost as the optimal route.

In some embodiments, the controller may output or store the candidateroutes, the optimal route, or both. For example, the controller maystore the candidate routes and the optimal route and may output theoptimal route to a trajectory controller, vehicle actuator, or acombination thereof, to operate the autonomous vehicle to travel fromthe origin to the target parking area using the optimal route.

In some embodiments, the autonomous vehicle may travel from the originto the target parking area using the optimal route at 5400. For example,the autonomous vehicle may include a vehicle actuator, such as theactuator 1240 shown in FIG. 1, and vehicle actuator may operate theautonomous vehicle to begin traveling from the origin to the targetparking area using the optimal route. In some embodiments, theautonomous vehicle may include a trajectory controller and thetrajectory controller may operate the autonomous vehicle to begintravelling based on the optimal route and current operatingcharacteristics of the autonomous vehicle, and the physical environmentsurrounding the autonomous vehicle.

In some embodiments, the optimal route may be updated. In someembodiments, updating the optimal route may include updating orregenerating the candidate routes and probability distributions, andidentifying the updated optimal route from the updated or regeneratedcandidate routes and probability distributions.

In some embodiments, the optimal route may be updated based on updatedvehicle transportation network information, based on differences betweenactual travel costs and the probable costs of the selected route, orbased on a combination of updated vehicle transportation networkinformation and differences between actual travel costs and the probablecosts of the selected route.

In some embodiments, the autonomous vehicle may receive current vehicletransportation network state information before or during travel. Insome embodiments, the autonomous vehicle may receive current vehicletransportation network state information, such as off-vehicle sensorinformation, from an off-vehicle sensor directly, or via a network, suchas the electronic communication network 2300 shown in FIG. 2. In someembodiments, the optimal route may be updated in response to receivingcurrent vehicle transportation network state information. For example,the current vehicle transportation network state information mayindicate a change of a state, such as a change from open to closed, of aportion of the vehicle transportation network that is included in theoptimal route, updating the candidate routes may include removingcandidate routes including the closed portion of the vehicletransportation network and generating new candidate routes andprobability distributions using the current location of the autonomousvehicle as the origin, and updating the optimal route may includeidentifying a new optimal route from the new candidate routes.

In some embodiments, the autonomous vehicle may complete traveling tothe target parking area from the current location of the autonomousvehicle using the updated optimal route.

In some implementations, identifying the vehicle transportation networkinformation at 5100 may include identifying the vehicle transportationnetwork information such that the vehicle transportation networkinformation includes parking area information representing parking areasin the vehicle transportation network, and parking area associationinformation, such as defined parking area association information andautomatically generated parking area association information, describingassociations between parking areas and destinations. Examples ofautomatically generating parking area association information are shownin FIGS. 6-7.

FIG. 6 is a diagram of a method of automatically generating parking areaassociation information in accordance with this disclosure. In someembodiments, automatically generating parking area associationinformation may include identifying vehicle operating information at6100, identifying supplementary vehicle location information at 6200,identifying non-vehicle operating information at 6300, correlatinglocation information at 6400, clustering candidate locations at 6500,associating parking areas with destinations at 6600, or a combinationthereof.

Although not shown separately in FIG. 6, automatically generatingparking area association information may include identifying operatinginformation, which may include the vehicle operating information, thesupplementary vehicle location information, the non-vehicle operatinginformation, or a combination thereof. In some embodiments, identifyingvehicle parking area association information may include filtering, orotherwise evaluating, the vehicle operating information, thesupplementary vehicle location information, the non-vehicle operatinginformation, or a combination thereof.

In some embodiments, vehicle operating information may be identified at6100. In some embodiments, the vehicle operating information may includeoperating information generated for one or more vehicles, which may bemanually operated vehicles, and may include vehicle probe data, vehiclelocation information, vehicle status information, vehicle eventinformation, vehicle bus data, such as controller area network (CAN)data, or any other information generated based on vehicle operation.

In some embodiments, the vehicle operating information may includeinformation reported by a vehicle, or an operational unit thereof, suchas a data logging unit, a telemetry unit, a probe unit, an operationalrecorder, or any other unit or combination of units capable ofdetecting, storing, or reporting a operation, or an operating condition,of a vehicle, such as a power-up operation, a start operation, a runningoperating condition, a stop operation, a power-down operation, a dooropening operation, a door open operating condition, a door closingoperation, a door closed operating condition, a door lock operation, adoor locked operating condition, a door unlock operation, a door unlockoperating condition, or any other operation or operating condition ofthe vehicle. In some embodiments, the vehicle operating information mayinclude a time, a date, a geographic location, or a combination thereof,for one or more of the operations, or operating conditions. In someembodiments, the vehicle operating information may be informationreported by the plurality of vehicles. For example, the vehicleoperating information may include records, each record may be associatedwith a vehicle identifier, and individual vehicles may be uniquelyidentified based on the vehicle identifiers.

In some embodiments, the vehicle operating information may includeinformation indicating vehicle operations. A vehicle operation mayinclude event indicators, which may include a type of vehicle operationor an event, such as start, stop, stand, park, door open, door close,load, or unload. A vehicle operation may include a date, a time, orboth. A vehicle operation may indicate a location, such as a GPSlocation within the vehicle transportation network. A vehicle operationmay include vehicle state information, such as a current number ofpassengers or occupancy, a change in occupancy, or a passenger presencestate. For example, the vehicle operating information may includeinformation reported by a vehicle, or an operational unit thereof, suchas a data logging unit, a telemetry unit, a probe unit, an operationalrecorder, or any other unit or combination of units capable ofdetecting, storing, or reporting a operation, or an operating condition,of a vehicle, such as a power-up operation, a start operation, a runningoperating condition, a stop operation, a power-down operation, a dooropening operation, a door open operating condition, a door closingoperation, a door closed operating condition, a door lock operation, adoor locked operating condition, a door unlock operation, a door unlockoperating condition, or any other operation or operating condition ofthe vehicle. In some embodiments, the vehicle operating information mayinclude a time, a date, a geographic location, or a combination thereof,for one or more of the operations, or operating conditions. For example,the vehicle operating information may indicate a vehicle operationincluding a stationary period, such as a period or duration between avehicle stop event and a subsequent vehicle start event, which may beidentified as a candidate parking operation, and a correspondinglocation may be identified as a candidate parking location.

In some embodiments, automatically generating the parking areaassociation information may include evaluating a sequence of eventsindicated in the vehicle operating information for a vehicle. Forexample, the vehicle operating information may include event indicatorsthat describe the sequence of events, which may include a stop event anda subsequent start event, and evaluating the sequence of events mayinclude determining the stationary period as a temporal differencebetween the stop event and the start event.

In some embodiments, the vehicle operating information may includepassenger information, such as a passenger identifier. For example, thevehicle operating information for a vehicle operation may includeinformation associating the vehicle operation with a passenger.

In some embodiments, identifying the vehicle operating information mayinclude filtering the vehicle operating information to identify one ormore candidate parking locations. For example, the vehicle operatinginformation may include information describing vehicle operations forthe plurality of vehicles, wherein each vehicle operation may beassociated with a respective vehicle, and identifying the vehicleoperating information may include filtering the vehicle operatinginformation to identify candidate parking operations, and correspondingcandidate parking locations, based on metrics, such as frequency of thelocation in the operating information associated with a vehicle,duration of an operation, or a group of operations, time period of anoperation, or group of operations, such as daytime, nighttime, morning,or evening. For example, a stationary period associated with a vehicleoperation may exceed a minimum parking duration, and the vehicleoperation may be identified as a candidate parking operation.

In some embodiments, parking area information may be automaticallygenerated based on vehicle operating information that includesinformation generated for a defined type of vehicle. For example, theoperating information may include a vehicle type indicator, which mayindicate whether a vehicle is a personal vehicle or a fleet vehicle,such as a taxi or a parcel delivery vehicle, and the operatinginformation may be filtered to omit operating information based on type,such as to omit fleet type vehicles. In another example, the operatinginformation may include a vehicle operating type indicator, which mayindicate whether a vehicle is a low occupancy carrier vehicle, such as avehicle operating as a taxi or a parcel delivery vehicle, and theoperating information may be filtered to omit operating information forvehicles based on operating type, such as to omit information forvehicles operating as low occupancy carrier vehicles.

In some embodiments, supplementary vehicle location information may beidentified at 6200. Supplementary vehicle location information mayinclude information reported by infrastructure devices in response todetecting a respective vehicle. For example, an infrastructure devicemay be a smart parking meter, a parking camera, a parking access device,or any other non-vehicle device associated with a parking area andcapable of detecting, or being detected by, a vehicle a defined parkingarea. The supplementary vehicle location information may includelocation information identifying a location of the infrastructure devicein the vehicle transportation network, such as a defined parking area.For example, a parking meter may identify a vehicle entering the parkingarea, may record a time, a date, or both, associated with the vehicleentering the parking area, may detect the vehicle exiting the parkingarea, may record a time, a date, or both of the vehicle exiting theparking area, and may report a supplementary parking operation for thevehicle indicating a defined location of the infrastructure device, thevehicle, the enter time, the exit time, or a combination thereof.

In some embodiments, candidate parking locations may be identified basedon a combination of the vehicle operating information and thesupplementary vehicle location information. For example, thesupplementary vehicle location information may indicate that a vehiclewas detected at a defined parking area during a period, the vehicleoperating information for the vehicle during the period may indicatethat the vehicle idled during the period, and the defined parking areamay be filtered or omitted from the candidate parking areas.

In some embodiments, non-vehicle operating information may be identifiedat 6300. Non-vehicle operating information may include informationreported for one or more users of a non-vehicle operating informationsystem or device and may include information indicating non-vehicleoperations. The non-vehicle operation information may include a date, atime, or both. A non-vehicle operation may indicate a location, such asa GPS location, a destination, or a point of interest.

In some embodiments, the non-vehicle operating information may includenon-vehicle operating information reported by a portable deviceassociated with a vehicle. For example, a portable device, such as asmartphone, carried by a passenger of the vehicle may includeinformation associating the passenger with the vehicle, and may includegeographic location information, such as GPS or assisted GPS (AGPS)information.

In some embodiments, the non-vehicle operating information may includeinformation reported by a third party computing system for a user. Forexample, the non-vehicle operating information may include informationfrom a social network, which may include status information for a user,which may indicate an association between the user and a definedlocation at a defined time and date. In some embodiments, thenon-vehicle operating information may include geo-tagged information,such as a geo-tagged tweet, a geo-tagged picture, or any othergeo-tagged non-vehicle operating information.

In some embodiments, the non-vehicle operating information may includeinformation identifying a location, such as a GPS location, that is notassociated with a destination or point of interest, and a destination orpoint of interest corresponding to the indicated location may beidentified from the vehicle transportation network information based onproximity.

In some embodiments, the non-vehicle operating information may befiltered, or otherwise processed, to identify destination operations.For example, a destination operation may be identified based on aduration of a period within a defined location, or a frequency ofinformation identifying a location within the defined location. In someembodiments, the non-vehicle operating information may includeinformation expressly indicating a destination location. For example,the non-vehicle operating information may include information indicatingan associating between the passenger and a defined location, such ascheck-in information, or information referencing the location, such as areview, or status information, which may include social network statusinformation.

In some embodiments, filtering the non-vehicle operating information mayinclude identifying a user from the non-vehicle operating information,identifying one or more destination operations for the user, identifyinga location corresponding to the destination operation as a candidatedestination location, or a combination thereof. In some embodiments,identifying the destination operation for the passenger may includeidentifying a non-vehicle operation associated with the user from thenon-vehicle operating information as the destination operation based ona stationary period associated with the non-vehicle operation. Forexample, the stationary period, which may indicate the period that theuser stayed within a defined location, may exceed a minimum destinationduration, and the corresponding operation, or group of operations, maybe identified as a destination operation.

In some embodiments, a candidate destination location may be identifiedbased on the vehicle operating information, the non-vehicle operatinginformation, or a combination of vehicle operating information andnon-vehicle operating information. For example, the vehicle operatinginformation may indicate a destination, and the indicated destinationmay be identified as a candidate destination location. In anotherexample, the vehicle operating information may indicate a selectedlocation that includes multiple identifiable destinations, and thenon-vehicle operating information may include information identifyingone of the identifiable destinations as a destination for a passenger ofthe vehicle.

In some embodiments, filtering the non-vehicle operating information mayinclude associating a non-vehicle operating information user identifier,such as a social media user name, with a passenger identifier associatedwith a vehicle. In some embodiments, the non-vehicle operatinginformation user identifier may be associated with the passengeridentifier associated with a vehicle based on user input, such as userinput identifying the association. For example, a passenger of a vehiclemay use a portable electronic device, such as a smartphone, to operate anon-vehicle operating information system, such as an application, toinput information associating a user identifier for the device, thenon-vehicle operating information system executing on the device, or fora third party non-vehicle operating information system, with the vehicleor with a passenger identifier associated with the passenger and thevehicle. In some embodiments, the non-vehicle operating information useridentifier may be associated with the passenger identifier associatedwith a vehicle automatically as shown in FIG. 7.

In some embodiments, location information may be correlated at 6400. Forexample, filtering the operating information at 6100/6200/6300 mayinclude identifying candidate parking operations and candidatedestination operations. The candidate parking operation information mayindicate corresponding temporal information, paring area information,passenger information, or a combination thereof. The candidatedestination operation information may indicate corresponding temporalinformation, destination location information, user information, or acombination thereof. The candidate parking operations may be associated,or correlated, with the candidate destination operations based ontemporal similarity and a defined association, or an automaticallygenerated association, between the passenger information from thecandidate parking operation and the user information from the candidatedestination operation.

In some embodiments, candidate locations may be clustered at 6500.Clustering candidate locations may include generating one or moreparking area clusters using, for example, spatial clustering. Forexample, a parking area cluster may include a spatially, orgeographically, proximate group of candidate parking locations. In someembodiments, parking areas may be identified based on the parking areaclusters and the vehicle transportation network information. Forexample, a location, a destination, or a point of interest may beidentified in the vehicle transportation network information spatially,or geographically, corresponding to a parking area cluster, and may beidentified as a parking area.

Similarly, clustering candidate locations may include generating one ormore destination location clusters using, for example, spatialclustering. For example, a destination location cluster may include aspatially, or geographically, proximate group of candidate destinationlocations. In some embodiments, destinations may be identified based onthe destination location clusters and the vehicle transportation networkinformation. For example, a location, a destination, or a point ofinterest may be identified in the vehicle transportation networkinformation spatially, or geographically, corresponding to a destinationlocation cluster, and may be identified as a destination.

In some embodiments, parking areas may be associated with destinationsat 6600. In some embodiments, associating parking areas withdestinations may include identifying a parking area, which maycorrespond with a parking area cluster identified at 6500, identifying adestination, which may correspond with a destination location clusteridentified at 6500, associating the parking area with the destinationbased on the correlation identified at 6400, and including parking areaassociation information in the parking area information describing theassociation between the parking area and the destination.

FIG. 7 is a diagram of a method of associating non-vehicle operatinginformation with vehicle operating information in accordance with thisdisclosure. In some embodiments, associating non-vehicle operatinginformation with vehicle operating information may include identifyingvehicle operating information at 7100, identifying non-vehicle operatinginformation at 7200, temporally grouping the information at 7300,spatially clustering the information at 7400, identifying tuples at7500, validating the tuples using temporal containment at 7600,identifying exclusive tuples at 7700, associating tuples at 7800, or acombination thereof.

In some embodiments, vehicle operating information may be identified at7100, which may be similar to identifying vehicle operating informationat 6100 as shown in FIG. 6, and non-vehicle operating information may beidentified at 7200, which may be similar to identifying non-vehicleoperating information at 6300 as shown in FIG. 6. For simplicity andclarity, the supplementary vehicle location information identified at6200 as shown in FIG. 6 is omitted from the description of FIG. 7;however, associating non-vehicle operating information with vehicleoperating information may include using the supplementary vehiclelocation information.

In some embodiments, the vehicle operating information and thenon-vehicle operating information may be temporally grouped at 7300. Insome embodiments, temporally grouping the information may includeidentifying information groups based on temporal information.Operations, or events, from the vehicle operating information and thenon-vehicle operating information may include temporal indicators withina defined period, such as a day, and may be temporally grouped based onthe defined period. For example, the operating information group for adefined date may include vehicle operating information for the date andnon-vehicle operating information for the date. In some embodiments, thetemporal grouping may be based on local time zone informationcorresponding to the geographic location indicated in the vehicle andnon-vehicle operating information. In some embodiments, the temporalinformation may be normalized, such as to a defined time zone or toCoordinated Universal Time (UTC). For simplicity and clarity, thevehicle operation information and the non-vehicle operating informationmay be collectively referred to herein as operating information.

In some embodiments, the information in each temporal group identifiedat 7300 may be spatially, or geographically, clustered at 7400. In someembodiments, the spatial clustering may be Delaunay Triangulation basedSpatial Clustering. For example, the operating information in thetemporal group for a defined date may include multiple spatial clusters,and each spatial cluster within a temporal group may include vehicleoperating information for the date and geographic location andnon-vehicle operating information for the date and geographic location.

In some embodiments, pairs, or tuples, of vehicle operations andnon-vehicle operations may be identified at 7500. In some embodiments,the tuples may be identified based on passenger identifiers associatedwith the vehicles in the vehicle operating information and useridentifiers associated with non-vehicle operations from the non-vehicleoperating information. For example, the operating information for aspatial cluster within a temporal group may include multiple passengeridentifiers and multiple user identifiers, and a tuple may be identifiedfor each combination of passenger identifier and user identifier.

In some embodiments, the tuples may be validated using temporalcontainment at 7600. Validating the tuples using temporal containmentmay include determining whether temporal information associated with thenon-vehicle operating information is within a duration associated withthe vehicle operating information, or whether a difference betweentemporal information associated with the non-vehicle operatinginformation and temporal information associated with the vehicleoperating information is within a threshold.

In an example, the vehicle operating information may identify a parkingoperation associated with a parking duration, a vehicle, a parking area,and a passenger of the vehicle; and the non-vehicle operatinginformation may include a social media message, such as a tweet,associated with a geo-tagged location, a temporal location, such as atime stamp, and a user identifier. The parking duration and the temporallocation may each correspond with a defined date, and the parkingoperation and the social media message may be included in a group forthe defined date. The distance between the parking area and thegeo-tagged location, for example, may be within a clustering threshold,and the parking operation and the social media message may be includedin a spatial cluster within the defined date. The temporal containmentmay determine that the temporal location is within the parking duration,and a tuple including passenger identifier associated with the parkingoperation and the user identifier associated with the social mediamessage may be identified as a candidate tuple.

In some embodiments, exclusive tuples may be identified at 7700.Identifying exclusive tuples may include filtering or omittingnon-exclusive tuples. For example, referring to example above, thevehicle operating information may include information identifyinganother parking operation associated with another vehicle associatedwith another passenger, which may be included in the group for thedefined date at 7300, may be included in the cluster at 7400, may beidentified as a tuple, with the user identifier, at 7500, may bevalidated at 7600, and both tuples may be omitted from the exclusivetuples at 7700. In another example, the non-vehicle operatinginformation may include information identifying another social mediamessage associated with another user, which may be included in the groupfor the defined date at 7300, may be included in the cluster at 7400,may be identified as a tuple, with the passenger identifier, at 7500,may be validated at 7600, and both tuples may be omitted from theexclusive tuples at 7700.

In some embodiments, associations between passenger identifiers and useridentifiers may be generated at 7800 based on the exclusive tuplesidentified at 7700. For example, the passenger identifier and the useridentifier for an exclusive tuple may be associated based on a count orcardinality of the number of times the tuple is identified as anexclusive tuple. For example, the number of times that a tuple isidentified as an exclusive tuple, which may include multiple dates,multiple locations, or both, may exceed a minimum association thresholdand the passenger identifier may be associated with the user identifier.

Although not shown in FIG. 7, the associations between passengeridentifiers and user identifiers generated at 7800 may be used toassociate parking areas with destinations or points of interest as shownin FIG. 6. For example, correlating location information at 6400 mayinclude correlating the vehicle operating information identified at 6100in FIG. 6 with the non-vehicle operating information identified at 6300based on the associations generated as shown in FIG. 7.

The above-described aspects, examples, and implementations have beendescribed in order to allow easy understanding of the disclosure are notlimiting. On the contrary, the disclosure covers various modificationsand equivalent arrangements included within the scope of the appendedclaims, which scope is to be accorded the broadest interpretation so asto encompass all such modifications and equivalent structure as ispermitted under the law.

What is claimed is:
 1. A vehicle comprising: a processor configured toexecute instructions stored on a non-transitory computer readable mediumto: identify transportation network information representing a vehicletransportation network, the vehicle transportation network including aprimary destination, wherein identifying the transportation networkinformation includes identifying the transportation network informationsuch that the transportation network information includes parking areainformation representing a plurality of parking areas, wherein eachparking area from the plurality of parking areas corresponds with arespective location in the vehicle transportation network, and such thatthe parking area information includes automatically generated parkingarea association information describing an association between at leastone parking area from the plurality of parking areas and the primarydestination, determine a target parking area from the plurality ofparking areas for the primary destination based on the transportationnetwork information, and identify a route from an origin to the targetparking area in the vehicle transportation network using thetransportation network information; and a trajectory controllerconfigured to operate the vehicle to travel from the origin to thetarget parking area using the route.
 2. The vehicle of claim 1, whereinthe processor is configured to execute instructions stored on thenon-transitory computer readable medium to: identify a docking locationfor the primary destination; and identify the route such that the routeincludes a first route portion from the origin to the docking locationand a second route portion from the docking location to the targetparking area.
 3. The vehicle of claim 1, wherein the processor isconfigured to execute instructions stored on the non-transitory computerreadable medium to identify the transportation network information suchthat the parking area information is based on operating information fora plurality of vehicles, wherein the operating information includes atleast one of vehicle operating information, supplementary vehiclelocation information, or non-vehicle operating information.
 4. Thevehicle of claim 3, wherein the processor is configured to executeinstructions stored on the non-transitory computer readable medium toidentify the transportation network information such that the vehicleoperating information is information reported by the plurality ofvehicles, wherein the vehicle operating information includes a pluralityof vehicle operations, wherein each vehicle operation from the pluralityof vehicle operations is associated with a respective vehicle from theplurality of vehicles, and wherein each parking area from the pluralityof parking areas corresponds with a respective vehicle operation fromthe plurality of vehicle operations.
 5. The vehicle of claim 3, whereinthe processor is configured to execute instructions stored on thenon-transitory computer readable medium to identify the transportationnetwork information such that the supplementary vehicle locationinformation is information reported by a plurality of infrastructuredevices in response to detecting a respective vehicle from the pluralityof vehicles, and wherein each infrastructure device from the pluralityof infrastructure devices is associated with a respective location inthe vehicle transportation network, and wherein the supplementaryvehicle location information includes a plurality of supplementaryvehicle parking locations, wherein each supplementary vehicle parkinglocation from the plurality of supplementary vehicle parking locationsis associated with a respective vehicle from the plurality of vehicles,and wherein each supplementary vehicle parking location from theplurality of supplementary vehicle parking locations corresponds with arespective parking operation from the plurality of vehicle operations.6. The vehicle of claim 5, wherein the processor is configured toexecute instructions stored on the non-transitory computer readablemedium to identify the transportation network information such that eachinfrastructure device from the plurality of infrastructure devices is asmart parking meter, a parking camera, or a parking access device. 7.The vehicle of claim 3, wherein the processor is configured to executeinstructions stored on the non-transitory computer readable medium toidentify the vehicle transportation network information such that thevehicle operating information includes passenger information thatidentifies a plurality of passengers such that each passenger from theplurality of passengers is associated with a respective vehicle from theplurality of vehicles.
 8. The vehicle of claim 7, wherein the processoris configured to execute instructions stored on the non-transitorycomputer readable medium to identify the transportation networkinformation such that the non-vehicle operating information includes aplurality of non-vehicle operations, wherein each non-vehicle operationfrom the plurality of non-vehicle operations includes: locationinformation reported by a portable device associated with a passengerfrom the plurality of passengers; or location information reported to athird party computing system for a user, wherein the non-vehicleoperating information includes an association between the user and apassenger from the plurality of passengers.
 9. The vehicle of claim 3,wherein the processor is configured to execute instructions stored onthe non-transitory computer readable medium to identify thetransportation network information such that the parking areainformation is determined by filtering the operating information. 10.The vehicle of claim 9, wherein filtering the operating informationincludes: identifying a vehicle from the plurality of vehicles;identifying a parking operation for the vehicle; and identifying alocation corresponding to the parking operation as a candidate parkinglocation.
 11. The vehicle of claim 10, wherein identifying the parkingoperation includes: on a condition that the operating informationincludes vehicle operating information, identifying the parkingoperation based on the vehicle operating information; on a conditionthat the operating information includes supplementary vehicle locationinformation, identifying the parking operation based on thesupplementary vehicle location information; and on a condition that theoperating information includes vehicle operating information andincludes supplementary vehicle location information, identifying theparking operation based on at least one of the vehicle operatinginformation or the supplementary vehicle location information.
 12. Thevehicle of claim 11, wherein identifying the parking operation based onthe vehicle operating information includes: identifying an operationassociated with the vehicle from the vehicle operating information asthe parking operation on a condition that a stationary period associatedwith the operation exceeds a minimum parking duration.
 13. The vehicleof claim 10, wherein filtering the operating information includes:identifying a passenger associated with the vehicle; identifying adestination operation for the passenger; and identifying a locationcorresponding to the destination operation as a destination location.14. The vehicle of claim 13, wherein identifying the destinationoperation for the passenger includes: identifying a non-vehicleoperation associated with the passenger from the non-vehicle operatinginformation as the destination operation on a condition that astationary period associated with the non-vehicle operation exceeds aminimum destination duration.
 15. The vehicle of claim 13, wherein theprocessor is configured to execute instructions stored on thenon-transitory computer readable medium to identify the transportationnetwork information such that the parking area information is determinedby: identifying a plurality of parking area clusters based on thevehicle operating information, wherein the each parking area clusterfrom the plurality of parking area clusters includes at least onecandidate parking location; and identifying a plurality of destinationlocation clusters based on the non-vehicle operating information,wherein the each destination location cluster from the plurality ofdestination location clusters includes at least one destinationlocation.
 16. The vehicle of claim 13, wherein the processor isconfigured to execute instructions stored on the non-transitory computerreadable medium to identify the transportation network information suchthat the parking area information is determined by: identifying theplurality of parking areas such that at least one parking area from theplurality of parking areas corresponds with a parking area cluster fromthe plurality of parking area clusters; identifying a plurality ofdestinations, such that the plurality of destinations includes theprimary destination, and such that at least one destinations from theplurality of destinations corresponds with a destination locationcluster from the plurality of destination location clusters; andincluding, in the parking area information, the automatically generatedparking area association information, such that the automaticallygenerated parking area association information describes at least oneassociation between a destination from the plurality of destinations anda parking area from the plurality of parking areas.
 17. The vehicle ofclaim 16, wherein the processor is configured to execute instructionsstored on the non-transitory computer readable medium to identify thetransportation network information such that the parking areaassociation information describes an automatically generated associationbetween the primary destination and a parking area from the plurality ofparking areas.
 18. The vehicle of claim 1, wherein the vehicle is anautonomous vehicle.
 19. A vehicle comprising: a processor configured toexecute instructions stored on a non-transitory computer readable mediumto: identify transportation network information representing a vehicletransportation network, the vehicle transportation network including aprimary destination, wherein identifying the transportation networkinformation includes identifying the transportation network informationsuch that the transportation network information includes parking areainformation representing a plurality of parking areas, wherein eachparking area from the plurality of parking areas corresponds with arespective location in the vehicle transportation network, and such thatthe parking area information includes automatically generated parkingarea association information describing an association between at leastone parking area from the plurality of parking areas and the primarydestination, such that the automatically generated parking areaassociation information is based on operating information for aplurality of vehicles and such that the automatically generated parkingarea association information is determined by filtering the operatinginformation, wherein filtering the operating information includes:identifying a vehicle from the plurality of vehicles, identifying aparking operation for the vehicle, identifying a location correspondingto the parking operation as a candidate parking location, wherein thecandidate parking location corresponds with the at least one parkingarea from the plurality of parking areas, identifying a passengerassociated with the vehicle, identifying a destination operation for thepassenger, identifying a location corresponding to the destinationoperation as a destination location, wherein a location of the primarydestination corresponds with the destination location, and including, inthe automatically generated parking area association information,automatically generated parking area association information thatdescribes an association between the primary destination and a parkingarea corresponding to the candidate parking location; determine a targetparking area from the plurality of parking areas for the primarydestination based on the transportation network information, andidentify a route from an origin to the target parking area in thevehicle transportation network using the transportation networkinformation; and a trajectory controller configured to operate thevehicle to travel from the origin to the target parking area using theroute.
 20. The vehicle of claim 19, wherein the processor is configuredto execute instructions stored on the non-transitory computer readablemedium to: identify a docking location for the primary destination; andidentify the route such that the route includes a first route portionfrom the origin to the docking location and a second route portion fromthe docking location to the target parking area.
 21. The vehicle ofclaim 19, wherein the processor is configured to execute instructionsstored on the non-transitory computer readable medium to identify thetransportation network information such that the operating informationincludes at least one of: vehicle operating information, wherein thevehicle operating information is information reported by the pluralityof vehicles, wherein the vehicle operating information includes aplurality of vehicle operations that includes the parking operation,wherein each vehicle operation from the plurality of vehicle operationsis associated with a respective vehicle from the plurality of vehicles,and wherein each parking area from the plurality of parking areascorresponds with a respective vehicle operation from the plurality ofvehicle operations, and wherein the vehicle operating informationincludes passenger information that identifies a plurality of passengerssuch that each passenger from the plurality of passengers is associatedwith a respective vehicle from the plurality of vehicles; supplementaryvehicle location information, wherein the supplementary vehicle locationinformation is information reported by a plurality of infrastructuredevices in response to detecting a respective vehicle from the pluralityof vehicles, and wherein each infrastructure device from the pluralityof infrastructure devices is associated with a respective location inthe vehicle transportation network, and wherein the supplementaryvehicle location information includes a plurality of supplementaryvehicle parking locations, wherein each supplementary vehicle parkinglocation from the plurality of supplementary vehicle parking locationsis associated with a respective vehicle from the plurality of vehicles,and wherein each supplementary vehicle parking location from theplurality of supplementary vehicle parking locations corresponds with arespective parking operation from the plurality of vehicle operations,or non-vehicle operating information, wherein the non-vehicle operatinginformation includes a plurality of non-vehicle operations, wherein eachnon-vehicle operation from the plurality of non-vehicle operationsincludes location information reported by a portable device associatedwith a passenger from the plurality of passengers or locationinformation reported to a third party computing system for a user,wherein the non-vehicle operating information includes an associationbetween the user and a passenger from the plurality of passengers. 22.The vehicle of claim 19, wherein the vehicle is an autonomous vehicle.